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From "Jun Xie (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-2251) MLLib Naive Bayes Example SparkException: Can only zip RDDs with same number of elements in each partition
Date Fri, 27 Jun 2014 02:50:24 GMT

    [ https://issues.apache.org/jira/browse/SPARK-2251?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14045462#comment-14045462
] 

Jun Xie commented on SPARK-2251:
--------------------------------

I use the following command:

git log

The first entry is 
commit 601032f5bfe2dcdc240bfcc553f401e6facbf5ec
Author: Zongheng Yang <zongheng.y@gmail.com>
Date:   Tue Jun 10 21:59:01 2014 -0700

How to find out the current version of my branch? My current branch is in master, and I add
Apache/Spark as the remote upstream.

Jun

> MLLib Naive Bayes Example SparkException: Can only zip RDDs with same number of elements
in each partition
> ----------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-2251
>                 URL: https://issues.apache.org/jira/browse/SPARK-2251
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.0.0
>         Environment: OS: Fedora Linux
> Spark Version: 1.0.0. Git clone from the Spark Repository
>            Reporter: Jun Xie
>            Assignee: Xiangrui Meng
>            Priority: Minor
>              Labels: Naive-Bayes
>             Fix For: 1.0.1
>
>
> I follow the exact code from Naive Bayes Example (http://spark.apache.org/docs/latest/mllib-naive-bayes.html)
of MLLib.
> When I executed the final command: 
> val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()
> It complains "Can only zip RDDs with same number of elements in each partition".
> I got the following exception:
> {code}
> 14/06/23 19:39:23 INFO SparkContext: Starting job: count at <console>:31
> 14/06/23 19:39:23 INFO DAGScheduler: Got job 3 (count at <console>:31) with 2 output
partitions (allowLocal=false)
> 14/06/23 19:39:23 INFO DAGScheduler: Final stage: Stage 4(count at <console>:31)
> 14/06/23 19:39:23 INFO DAGScheduler: Parents of final stage: List()
> 14/06/23 19:39:23 INFO DAGScheduler: Missing parents: List()
> 14/06/23 19:39:23 INFO DAGScheduler: Submitting Stage 4 (FilteredRDD[14] at filter at
<console>:31), which has no missing parents
> 14/06/23 19:39:23 INFO DAGScheduler: Submitting 2 missing tasks from Stage 4 (FilteredRDD[14]
at filter at <console>:31)
> 14/06/23 19:39:23 INFO TaskSchedulerImpl: Adding task set 4.0 with 2 tasks
> 14/06/23 19:39:23 INFO TaskSetManager: Starting task 4.0:0 as TID 8 on executor localhost:
localhost (PROCESS_LOCAL)
> 14/06/23 19:39:23 INFO TaskSetManager: Serialized task 4.0:0 as 3410 bytes in 0 ms
> 14/06/23 19:39:23 INFO TaskSetManager: Starting task 4.0:1 as TID 9 on executor localhost:
localhost (PROCESS_LOCAL)
> 14/06/23 19:39:23 INFO TaskSetManager: Serialized task 4.0:1 as 3410 bytes in 1 ms
> 14/06/23 19:39:23 INFO Executor: Running task ID 8
> 14/06/23 19:39:23 INFO Executor: Running task ID 9
> 14/06/23 19:39:23 INFO BlockManager: Found block broadcast_0 locally
> 14/06/23 19:39:23 INFO BlockManager: Found block broadcast_0 locally
> 14/06/23 19:39:23 INFO HadoopRDD: Input split: file:/home/jun/open_source/spark/mllib/data/sample_naive_bayes_data.txt:0+24
> 14/06/23 19:39:23 INFO HadoopRDD: Input split: file:/home/jun/open_source/spark/mllib/data/sample_naive_bayes_data.txt:24+24
> 14/06/23 19:39:23 INFO HadoopRDD: Input split: file:/home/jun/open_source/spark/mllib/data/sample_naive_bayes_data.txt:0+24
> 14/06/23 19:39:23 INFO HadoopRDD: Input split: file:/home/jun/open_source/spark/mllib/data/sample_naive_bayes_data.txt:24+24
> 14/06/23 19:39:23 ERROR Executor: Exception in task ID 9
> org.apache.spark.SparkException: Can only zip RDDs with same number of elements in each
partition
> 	at org.apache.spark.rdd.RDD$$anonfun$zip$1$$anon$1.hasNext(RDD.scala:663)
> 	at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:388)
> 	at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1067)
> 	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:858)
> 	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:858)
> 	at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1079)
> 	at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1079)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:51)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> 	at java.lang.Thread.run(Thread.java:724)
> 14/06/23 19:39:23 ERROR Executor: Exception in task ID 8
> org.apache.spark.SparkException: Can only zip RDDs with same number of elements in each
partition
> 	at org.apache.spark.rdd.RDD$$anonfun$zip$1$$anon$1.hasNext(RDD.scala:663)
> 	at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:388)
> 	at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1067)
> 	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:858)
> 	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:858)
> 	at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1079)
> 	at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1079)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:51)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> 	at java.lang.Thread.run(Thread.java:724)
> 14/06/23 19:39:23 WARN TaskSetManager: Lost TID 8 (task 4.0:0)
> 14/06/23 19:39:23 WARN TaskSetManager: Loss was due to org.apache.spark.SparkException
> org.apache.spark.SparkException: Can only zip RDDs with same number of elements in each
partition
> 	at org.apache.spark.rdd.RDD$$anonfun$zip$1$$anon$1.hasNext(RDD.scala:663)
> 	at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:388)
> 	at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1067)
> 	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:858)
> 	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:858)
> 	at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1079)
> 	at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1079)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:51)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> 	at java.lang.Thread.run(Thread.java:724)
> 14/06/23 19:39:23 ERROR TaskSetManager: Task 4.0:0 failed 1 times; aborting job
> 14/06/23 19:39:23 INFO TaskSchedulerImpl: Removed TaskSet 4.0, whose tasks have all completed,
from pool 
> 14/06/23 19:39:23 INFO DAGScheduler: Failed to run count at <console>:31
> 14/06/23 19:39:23 INFO TaskSetManager: Loss was due to org.apache.spark.SparkException:
Can only zip RDDs with same number of elements in each partition [duplicate 1]
> 14/06/23 19:39:23 INFO TaskSchedulerImpl: Removed TaskSet 4.0, whose tasks have all completed,
from pool 
> 14/06/23 19:39:23 INFO TaskSchedulerImpl: Cancelling stage 4
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 4.0:0 failed
1 times, most recent failure: Exception failure in TID 8 on host localhost: org.apache.spark.SparkException:
Can only zip RDDs with same number of elements in each partition
>         org.apache.spark.rdd.RDD$$anonfun$zip$1$$anon$1.hasNext(RDD.scala:663)
>         scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:388)
>         org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1067)
>         org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:858)
>         org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:858)
>         org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1079)
>         org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1079)
>         org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
>         org.apache.spark.scheduler.Task.run(Task.scala:51)
>         org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
>         java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>         java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>         java.lang.Thread.run(Thread.java:724)
> Driver stacktrace:
> 	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1038)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1022)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1020)
> 	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> 	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1020)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:638)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:638)
> 	at scala.Option.foreach(Option.scala:236)
> 	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:638)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1212)
> 	at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> 	at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> 	at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> 	at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> 	at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
> 	at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
> 	at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
> 	at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
> 	at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
> {code}



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